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Teachers Curriculum - Introduction to Machine Learning Algorithms

Suitable for Educators Introducing AI in Classrooms

Teach Artificial Intelligence (AI) in your classrooms effortlessly using AIClub curriculums! We provide progressive curriculums with a wide range and depth. They include lesson guides, videos, presentation material, exercises and assessments, as well as online support. All the material is available online in your account!

This unit provides a gentle introduction to AI algorithms (a) K-Nearest neighbors (b) Linear Regression (c) K-Means clustering and (d) Neural Networks. We recommend teaching the introductory units before teaching this unit in your classroom (listed below).

Progressive Curriculum

Below are listed a subset of the huge array of curriculums provided by AIClub. You can also explore the corresponding book for them here

Other Curriculums


The Fundamentals of Artificial Intelligence

Curriculum contains an introduction to artificial intelligence providing context how we are already using it in our daily life. It explains how AIs learn from data and the different sources from which data is acquired.

Keyboard and Mouse

Introduction to AI

This unit provides a gentle introduction to AI and can be taught by educators with varying backgrounds! It covers how AI learns, data, statistics and ethics. No math or programming pre-requisites.


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AI Ethics

This unit focuses on AI Ethics and is a subset of the curriculum provided in Introduction to AI. It can be taught by educators with varying backgrounds! 



Introduction to Machine learning

This unit provides a gentle introduction to two types of AI algorithms, classification and regression. It covers the metrics used to measure their performance and has several hands-on exercises. You can find more information here.


Get Introduced to Artificial Intelligence and Machine Learning Algorithms 

Artificial Intelligence (AI) is how Google search, Alexa, Siri, auto-correct, speech translation, face recognition, self-driving cars, etc. learn from data and humans. Teach this course effortlessly using AIClub curriculums.

Why our curriculum?

• Designed by AI experts with PhDs in Computer Science!

• Only workshops where students build AIs in their first class! Students love building AIs and learning how they work!

We have no math or programming requirement. If they would like to code, they can do that also! 
Please see our brochure for more info about our programs!


This AI course for educators covers the fundamentals of AI, including key concepts and various types of AI such as supervised and unsupervised learning. Specific techniques, including k-nearest neighbors, regression, and neural networks, will also be covered in depth, with interactive videos, discussion points, and coding snippets provided to help educators engage with the material and understand how to apply the concepts in their classrooms.

There is no math or programming prerequisite to teach this class!

Topics Covered:

Recap of the fundamentals of AI

  - Introduction to AI

  - Hierarchy of AI terms

  - Benefits and challenges of AI

  - How does an AI learn?

  - Recap Regression vs Classification

  - How Navigator is built on Cloud

K-Nearest Neighbors

   - Introduction to the KNN algorithm

   - Exercise - House Prices

   - Demo: KNN

   - AI in Real Life - Dynamic Pricing

Linear Regression

    - Linear regression

    - Averages Exercise

    - Recap RMSE vs MAE

    - Demo: Linear Regression

    - Product Exercise

    - Regression for Curves

    - Curve Exercise

    - Compare KNN and Linear Regression

 K-Means Clustering

    - Unsupervised learning

    - Introduction to Clustering

    - K-means clustering

    - AI in Real Life - Fraud Detection

Neural Network

    - How to think about images

    - Introduction to Deep Learning - MLP

    - Flattening images

    - Introduction to MNIST dataset

    - Train MLP with flattened images

    - Parameters of MLP

    - Exercise - Hyper-parameter tuning in MLP

    - Train directly with color images

 Python Concepts

    - Data-types and input/output

    - Loops and conditionals

    - Lists

    - Modules - Pandas

    - Scikit-learn

 Python Exercises

    - K-nearest neighbors classification

    - K-nearest neighbors regression

    - Linear Regression

    - K-means clustering

    - Convert a color image to grayscale

    - Resize an image

    - Flatten a grayscale image

    - MNIST and MLP

What teachers take away

• A good understanding of specific AI algorithms - K-nearest neighbors, linear regression, K-means clustering, neural networks.

• Everything they need to teach AI in a classroom.

What students have accomplished after using these curriculums!